

from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser



@dataclass
class        A__:
						"""simple docstring"""






						_A    :				str       					=			field(
						    metadata={'''help''': '''The output directory where the model will be written.'''},  )
						_A    :				str       					=			field(
						    metadata={
						        '''help''': (
						            '''The encoder model checkpoint for weights initialization.'''
						            '''Don\'t set if you want to train an encoder model from scratch.'''
						        )
						    },  )
						_A    :				str       					=			field(
						    metadata={
						        '''help''': (
						            '''The decoder model checkpoint for weights initialization.'''
						            '''Don\'t set if you want to train a decoder model from scratch.'''
						        )
						    },  )
						_A    :				Optional[str]       					=			field(
						    default=_UpperCAmelCase,  metadata={'''help''': '''Pretrained encoder config name or path if not the same as encoder_model_name'''}							)
						_A    :				Optional[str]       					=			field(
						    default=_UpperCAmelCase,  metadata={'''help''': '''Pretrained decoder config name or path if not the same as decoder_model_name'''}							)







def 					_UpperCAmelCase				(						):

			'''simple docstring'''



			a_						:						Optional[Any] 							= HfArgumentParser((ModelArguments,))
			((a_	)							,						)						:						List[str] 							= parser.parse_args_into_dataclasses()

			# Load pretrained model and tokenizer

			# Use explicit specified encoder config
			if model_args.encoder_config_name:
						a_						:						List[str] 							= AutoConfig.from_pretrained(model_args.encoder_config_name)
			# Use pretrained encoder model's config
			else:
						a_						:						Any 							= AutoConfig.from_pretrained(model_args.encoder_model_name_or_path)

			# Use explicit specified decoder config
			if model_args.decoder_config_name:
						a_						:						Union[str, Any] 							= AutoConfig.from_pretrained(model_args.decoder_config_name)
			# Use pretrained decoder model's config
			else:
						a_						:						Union[str, Any] 							= AutoConfig.from_pretrained(model_args.decoder_model_name_or_path)

			# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
			a_						:						Optional[Any] 							= True
			a_						:						str 							= True

			a_						:						Union[str, Any] 							= FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
			    encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path  ,	decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path  ,	encoder_config=a__  ,	decoder_config=a__  ,	)

			# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
			a_						:						str 							= decoder_config.decoder_start_token_id
			a_						:						List[str] 							= decoder_config.pad_token_id
			if decoder_start_token_id is None:
						a_						:						Union[str, Any] 							= decoder_config.bos_token_id
			if pad_token_id is None:
						a_						:						Dict 							= decoder_config.eos_token_id

			# This is necessary to make Flax's generate() work
			a_						:						Dict 							= decoder_config.eos_token_id
			a_						:						List[Any] 							= decoder_start_token_id
			a_						:						List[Any] 							= pad_token_id

			a_						:						str 							= AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path)

			a_						:						List[str] 							= AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path)
			a_						:						List[str] 							= tokenizer.convert_ids_to_tokens(model.config.pad_token_id)

			model.save_pretrained(model_args.output_dir)
			image_processor.save_pretrained(model_args.output_dir)
			tokenizer.save_pretrained(model_args.output_dir)


if __name__ == "__main__":
	main()



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					UNetaDModel
			__a    :					ScoreSdeVeScheduler





			def __init__(  self ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   Union[str, Any]:

							'''simple docstring'''


							super().__init__()
							self.register_modules(unet=lowerCAmelCase__ ,  scheduler=lowerCAmelCase__		)





			@torch.no_grad()
			def __call__(  self ,  lowerCAmelCase__ = 1 ,  lowerCAmelCase__ = 20_00 ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = "pil" ,  lowerCAmelCase__ = True ,  **lowerCAmelCase__ ,  )	->   Union[ImagePipelineOutput, Tuple]:

							'''simple docstring'''


							__lowercase					     =				self.unet.config.sample_size
							__lowercase					     =				(batch_size, 3, img_size, img_size)

							__lowercase					     =				self.unet

							__lowercase					     =				randn_tensor(lowerCAmelCase__ ,  generator=lowerCAmelCase__		) * self.scheduler.init_noise_sigma
							__lowercase					     =				sample.to(self.device		)

							self.scheduler.set_timesteps(lowerCAmelCase__		)
							self.scheduler.set_sigmas(lowerCAmelCase__		)

							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
											__lowercase					     =				self.scheduler.sigmas[i] * torch.ones(shape[0] ,  device=self.device		)

											# correction step
											for _ in range(self.scheduler.config.correct_steps		):
															__lowercase					     =				self.unet(lowerCAmelCase__ ,  lowerCAmelCase__		).sample
															__lowercase					     =				self.scheduler.step_correct(lowerCAmelCase__ ,  lowerCAmelCase__ ,  generator=lowerCAmelCase__		).prev_sample

											# prediction step
											__lowercase					     =				model(lowerCAmelCase__ ,  lowerCAmelCase__		).sample
											__lowercase					     =				self.scheduler.step_pred(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  generator=lowerCAmelCase__		)

											__lowercase     ,	__lowercase					     =				output.prev_sample, output.prev_sample_mean

							__lowercase					     =				sample_mean.clamp(0 ,  1		)
							__lowercase					     =				sample.cpu().permute(0 ,  2 ,  3 ,  1		).numpy()
							if output_type == "pil":
											__lowercase					     =				self.numpy_to_pil(lowerCAmelCase__		)

							if not return_dict:
											return (sample,)

							return ImagePipelineOutput(images=lowerCAmelCase__		)